Google releases Agent Executor to address production reliability gaps
Google has introduced Agent Executor, an open source runtime designed to help enterprises run AI agents reliably at scale. The move reflects a shift in the industry from building prototype agents to managing the operational challenges of running them in production.
The runtime includes durable execution, allowing workflows to resume after outages or human approvals. It also provides secure sandboxing to isolate agent components, session consistency controls for distributed workflows, and connection recovery features to preserve execution state during network interruptions.
Developers can use "trajectory branching" to test alternate execution paths from saved checkpoints without losing prior context. Agent Executor also supports multiple deployment models, including on-premises and pre-built or custom managed agents.
What production teams actually need
Advait Patel, senior reliability engineer at Broadcom, said the runtime addresses real blockers for enterprise production agents. "Durability, orchestration, and resumability are the real blockers for any enterprise production agents," he said.
Existing frameworks like LangChain and AutoGen work well for prototyping but often fail in production when agents run for hours or days. "What kills enterprise adoption is agents that lose their state when a pod restarts, sessions that corrupt under concurrent writes, or long running workflows that cannot recover from a network blip," Patel said.
For CIOs, the runtime's operational safeguards such as secure sandboxing and checkpointing matter for incident analysis and auditability. Gaurav Dewan, research director at Avasant, cautioned that the runtime alone does not solve broader governance challenges.
"Issues such as accountability, explainability of agent decisions, policy enforcement, and secure access across interconnected systems are still evolving," Dewan said. "While distributed runtimes can strengthen the operational backbone of agent deployments, CIO-level considerations around trust, compliance, and enterprise control are likely to require additional governance and oversight layers beyond runtime infrastructure alone."
Hyperscalers compete for the infrastructure layer
Google is not alone in shaping the emerging infrastructure for enterprise AI agents. Microsoft promotes AutoGen, while AWS offers Bedrock AgentCore.
All three are converging on a similar strategy: offer open or interoperable tooling to drive developer adoption, then monetize through underlying infrastructure. "Google, Microsoft, and AWS are increasingly offering SDKs, agent frameworks, and orchestration tools to drive developer adoption and ecosystem growth, while continuing to generate value through compute infrastructure, managed AI platforms, data services, and observability capabilities," Dewan said.
Patel compared Google's approach to its strategy with Kubernetes a decade ago. "Give away the runtime, and drive consumption on Google Cloud via services, such as the Gemini Enterprise Agent Platform and Managed Agents API," he said.
Hyperscalers have learned that proprietary agent frameworks will not gain enterprise adoption. "The money is in cloud consumption, managed services, and model inference," Patel said. "The tools on top need to be open or nobody will trust them."
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